AIMC Topic: Putamen
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Machine Learning-Based Diagnostic Prediction Model Using T1-Weighted Striatal Magnetic Resonance Imaging for Early-Stage Parkinson's Disease Detection.
Academic radiology
Apr 18, 2025
RATIONALE AND OBJECTIVES: Diagnosing Parkinson's disease (PD) typically relies on clinical evaluations, often detecting it in advanced stages. Recently, artificial intelligence has increasingly been applied to imaging for neurodegenerative disorders....
An rs-fMRI based neuroimaging marker for adult absence epilepsy.
Epilepsy research
Jun 28, 2024
OBJECTIVE: Approximately 20-30 % of epilepsy patients exhibit negative findings on routine magnetic resonance imaging, and this condition is known as nonlesional epilepsy. Absence epilepsy (AE) is a prevalent form of nonlesional epilepsy. This study ...
Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy.
European radiology
May 1, 2023
OBJECTIVES: The precise segmentation of atrophic structures remains challenging in neurodegenerative diseases. We determined the performance of a Deep Neural Patchwork (DNP) in comparison to established segmentation algorithms regarding the ability t...
Predicting alcohol dependence from multi-site brain structural measures.
Human brain mapping
Oct 16, 2020
To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored ...
The neuromorphological caudate-putaminal clustering of neostriate interneurons: Kohonen self-organizing maps and supervised artificial neural networks with multivariate analysis.
Journal of theoretical biology
Nov 21, 2017
AIMS: The objective of this study is to investigate the possibility of the neuromorphotopological clustering of neostriate interneurons (NSIN) and their consequent classification into caudate (CIN) and putaminal neuron type (PIN), according to the nu...
Right putamen and age are the most discriminant features to diagnose Parkinson's disease by using I-FP-CIT brain SPET data by using an artificial neural network classifier, a classification tree (ClT).
Hellenic journal of nuclear medicine
Jan 1, 2017
OBJECTIVE: The differential diagnosis of Parkinson's disease (PD) and other conditions, such as essential tremor and drug-induced parkinsonian syndrome or normal aging brain, represents a diagnostic challenge. I-FP-CIT brain SPET is able to contribut...
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